{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c18d947e-c58c-4c64-9994-e7c605e25de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "raw_df=np.loadtxt('logi-y.txt',delimiter=',',encoding='utf-8')\n",
    "data=raw_df[:,0:2]\n",
    "target=raw_df[:,2]\n",
    "x,y=data,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=1,test_size=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eac72941-1d7f-4524-ad8f-94d254c7112b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型预测正确率: 0.8666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)\n",
    "ac=accuracy_score(y_test,model.predict(x_test))\n",
    "print(\"模型预测正确率:\",ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2044bceb-1a00-4e43-bb62-16f869d7b432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "N,M=500,500\n",
    "t1=np.linspace(0,100,N)\n",
    "t2=np.linspace(0,100,M)\n",
    "x1,x2=np.meshgrid(t1,t2)\n",
    "x_new=np.stack((x1.flat,x2.flat),axis=1)\n",
    "y_predict=model.predict(x_new)\n",
    "y_hat=y_predict.reshape(x1.shape)\n",
    "iris_cmap=ListedColormap([\"#ACF080\",\"#A0A0FF\"])\n",
    "plt.pcolormesh(x1,x2,y_hat,cmap=iris_cmap)\n",
    "plt.scatter(x[y==0,0],x[y==0,1],s=60,c='b',marker='o')\n",
    "plt.scatter(x[y==1,0],x[y==1,1],s=60,c='r',marker='^')\n",
    "plt.xlabel(\"科目1\")\n",
    "plt.ylabel(\"科目2\")\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "797f6a03-5268-4a6e-8d06-94649457a6e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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